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When Computational Fluid Dynamics (CFD) is used in the development of road vehicles for passenger and performance use, the fidelity and numerical accuracy of the simulation are paramount as manufacturers strive to optimize the vehicle down to the single aerodynamic count. While much research has been performed on how the choices of simulation model or grid size affects the simulation results, very little has been done to investigate how the spatial decomposition of the domain amongst different nodes of a high-performance computing unit (HPC) influences the results of the simulation. As simulations grow larger, more nodes are required to reduce the simulation time, however in most commercial software this introduces a new form of error due to the accumulation of round-off errors created in the intra-node communication schemes used during iterations.

Faster turn-around times and cost-effectiveness make the Reynolds Averaged Navier-Stokes (RANS) simulation approach still a widely utilized tool in racecar aerodynamic development, an industry where a large volume of simulations and short development cycles are constantly demanded. However, a well-known flaw of the RANS methodology is its inability to properly characterize the separated and wake flow associated with complex automotive geometries using the existing turbulence models. Experience suggests that this limitation cannot be overcome by simply refining the meshing schemes alone. Some earlier researches have shown that the closure coefficients involved in the RANS turbulence modeling transport equations most times influence the simulation prediction results.